Machine learning is a fast-growing and successful branch of artificial intelligence. In essence, machine learning is the process of allowing a computer system to teach itself how to perform complex tasks by analyzing large sets of data, rather than being explicitly programmed with a particular algorithm or solution.

In this way, machine learning enables a computer to learn how to perform a task on its own and to continue to optimize its approach over time, without direct human input.

In other words, it's the computer that is creating the algorithm, not the programmers, and often these algorithms are sufficiently complicated that programmers can't explain how the computer is solving the problem. Humans can't trace the computer's logic from beginning to end; they can only determine if it's finding the right solution to the assigned problem, which is output as a "prediction."

There are several different approaches to training expert systems that rely on machine learning, specifically "deep" learning that functions through the processing of computational nodes. Here are the most common forms:

Supervised learning is a model in which computers are given data that has already been structured by humans. For example, computers can learn from databases and spreadsheets in which the data has already been organized, such as financial data or geographic observations recorded by satellites.

Unsupervised learning uses databases that are mostly or entirely unstructured. This is common in situations where the data is collected in a way that humans can't easily organize or structure it. A common example of unstructured learning is spam detection, in which a computer is given access to enormous quantities of emails and it learns on its own to distinguish between wanted and unwanted mail.

Reinforcement learning is when humans monitor the output of the computer system and help guide it toward the optimal solution through trial and error. One way to visualize reinforcement learning is to view the algorithm as being "rewarded" for achieving the best outcome, which helps it determine how to interpret its data more accurately.

The field of machine learning is very active right now, with many common applications in business, academia, and industry. Here are a few representative examples:

Recommendation engines use machine learning to learn from previous choices people have made. For example, machine learning is commonly used in software like video streaming services to suggest movies or TV shows that users might want to watch based on previous viewing choices, as well as "you might also like" recommendations on retail sites.

Banks and insurance companies rely on machine learning to detect and prevent fraud through subtle signals of strange behavior and unexpected transactions. Traditional methods for flagging suspicious activity are usually very rigid and rules-based, which can miss new and unexpected patterns, while also overwhelming investigators with false positives. Machine learning algorithms can be trained with real-world fraud data, allowing the system to classify suspicious fraud cases far more accurately.

Inventory optimization a part of the retail workflow is increasingly performed by systems trained with machine learning. Machine learning systems can analyze vast quantities of sales and inventory data to find patterns that elude human inventory planners. These computer systems can make more accurate probability forecasting for customer demand.

Machine automation increasingly relies on machine learning. For example, self-driving car technology is deeply indebted to machine learning algorithms for the ability to detect objects on the road, classify those objects, and make accurate predictions about their potential movement and behavior.

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What is machine learning? Here's what you need to know - Business Insider - Business Insider

For decades, artificial intelligence and machine learning have advanced at a rapid pace. Today, there are many ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives, such as social media, shopping recommendations, email spam detection, speech recognition, self-driving cars, UAVs, and so on.

The simulation of human intelligence is programmed to think like humans and mimic our actions to achieve a specific goal. In our own field, machine learning has also changed the ways to solve navigation problems and taken on a significant role in advancing PNT technologies in the future.

LI-TA HSU, HONG KONG POLYTECHNIC UNIVERSITY

Q: Can machine learning replace conventional GNSS positioning techniques?

Actually, it makes no sense to use ML when the exact physics/mathematical models of GNSS positioning are known, and when using machine learning (ML) techniques over any appreciable area to collect extensive data and train the network to estimate receiver locations would be an impractically large undertaking. We, human beings, designed the satellite navigation systems based on the laws of physics discovered. For example, we use Keplers laws to model the position of satellites in an orbit. We use the spread-spectrum technique to model the satellite signal allowing us to acquire very weak signals transmitted from the medium-Earth orbits. We understand the Doppler effect and design tracking loops to track the signal and decode the navigation message. We finally make use of trilateration to model the positioning and use the least square to estimate the location of the receiver. By the efforts of GNSS scientists and engineers for the past several decades, GNSS can now achieve centimeter-level positioning. The problem is; if everything is so perfect, why dont we have a perfect GNSS positioning?

The answer for me as an ML specialist is that the assumptions made are not always valid in all contexts and applications! In trilateration, we assume the satellite signal always transmitted in direct line-of-sight (LOS). However, different layers in the atmosphere can diffract the signal. Luckily, remote-sensing scientists studied the troposphere and ionosphere and came up with sophisticated models to mitigate the ranging error caused by transmission delay. But the multipath effects and non-line-of-sight (NLOS) receptions caused by buildings and obstacles on the ground are much harder to deal with due to their high nonlinearity and complexity.

Q: What are the challenges of GNSS and how can machine learning help with it?

GNSS performs very differently under different contexts. Context means what and where. For example, a pedestrian walks in an urban canyon or a pedestrian sits in a car that drives in a highway. The notorious multipath and NLOS play major roles to affect the performance GNSS receiver under different context. If we follow the same logic of the ionospheric research to deal with the multipath effect, we need to study 3D building models which is the main cause of the reflections. Extracting from our previous research, the right of Figure 1 is simulated based on the LOD1 building model and single-reflected ray-tracing algorithm. It reveals the positioning error caused by the multipath and NLOS is highly site-dependent. In other words, the nonlinearity and complexity of multipath and NLOS are very high.

Generally speaking, ML derives a model based on data. What exactly does ML do best?

Phenomena we simply do not know how to model by explicit laws of physics/math, for example, contexts and semantics.

Phenomena with high complexity, time variance and nonlinearity.

Looking at the challenges of GNSS multipath and the potential of ML, it becomes straightforward to apply artificial intelligence to mitigate multipath and NLOS. One mainstream idea is to use ML to train the models to classify LOS, multipath and NLOS measurements. This idea is illustrated in Figure 2. Three-steps, data labeling, classifier training, and classifier evaluation, are required. In fact, there are also challenges in each step.

Are we confident in our labeling?

In our work, we use 3D city models and ray-tracing simulation to label the measurements we received from the GNSS receiver. The label may not be 100% correct since the 3D models are not conclusive enough to represent the real world. Trees and dynamic objects (vehicles and pedestrians) are not included. In addition, the multiple reflected signals are very hard to trace and the 3D models could have errors.

What are the classes and features?

For the classes, popular selections are the presence (binary) of multipath or NLOS and their associated pseudorange errors. The features are selected based on the variables that are affected by multipath, including carrier-to-noise ratio, pseudorange residual, DOP, etc. If we can assess a step deeper into the correlator, the shape of correlators in code and carrier are also excellent features. Our study evaluates the comparison between the different levels (correlator, RINEX, and NMEA) of features for the GNSS classifier and reveals that the rawer the feature it is, the better classification accuracy can be obtained. Finally, the methods of exploratory data analysis, such as principle component analysis, can better select the features that are more representative to the class.

Are we confident that the data we used to train the classifier are representative enough for the general application cases?

Overfitting of the data is always being a challenge for ML. Multipath and NLOS effects are very difficult in different cities. For example, the architectures in Europe and Asia are very different, producing different multipath effects. Classifiers trained using the data in Hong Kong do not necessarily perform well in London. The categorization of cities or urban areas in terms of their effects on GNSS multipath and NLOS is still an open question.

Q: What are the challenges of integrated navigation systems and how can machine learning can help with them?

Seamless positioning has always been the ultimate goal. However, each sensor has a different performance in different areas. Table 1 gives a rough picture. Inertial sensors seem to perform stably in most areas. But the MEMS-INS suffers from drift and is highly affected by the random noise caused by the temperature variations. Naturally, integrated navigation is a solution. The sensor integration, in fact, shall be regarded in both long-term and short-term.

Long-term Sensor SelectionIn the long term, available sensors for positioning are generally more than enough. The determination of the best subsets of sensors to integrate is the question to ask. Consider an example of seamless positioning for a city dweller travelling from home to the office:

Walking on a street to the subway station (GNSS+IMU)

Walking in a subway station (Wi-Fi/BLE+IMU)

Traveling on a subway (IMU)

Walking in an urban area to the office (VPS+ GNSS+ Wi-Fi/BLE+IMU)

This example clearly shows that seamless positioning should integrate different sensors. The selection of the sensors can be done heuristically or by maximizing the observability of sensors. If the sensors are selected heuristically, we must have the ability to know what context the system is operating under. This is one of the best angles for ML to cut in. In fact, the classification of the scenarios or contexts is exactly what ML does best. A recently published journal paper demonstrates how to detect different contexts using smartphone sensors for context-adaptive navigation (Gao and Groves 2020). Sensors in smartphones are used in the models trained by supervised ML to determine not only the environment but also the behavior (such as transportation modes, including static, pedestrian walk, and sitting on a car or a subway, etc.).

According to their result, the state-of-the-art detection algorithm can achieve over 95% for pedestrians under indoor, intermediate, and outdoor scenarios. This finding encourages the use of ML to intelligently select the right navigation systems for an integrated navigation system under different areas. The same methodology can be easily extended to vehicular applications with a proper modification in the selections of features, classes, and machine learning algorithms.

Short-term Sensor Weighting

Technically speaking, an optimal integrated solution can be obtained if the uncertainty of the sensor can be optimally described. Presumably, the sensors uncertainty remains unchanged under a certain environment. As a result, most of the sensors uncertainty is carefully calibrated before its use in integration systems.

However, the problem is that the environment could change rapidly within a short period of time. For example, a car drives in an urban area with several viaducts or a car drives in an open sky with a canopy of foliage. These scenarios affect the performance of GNSS greatly, however, the affecting periods were too short to exclude the GNSS from the subset of sensors used. The best solution against these unexpected and transient effects are de-weighting the affected sensors in the system.

Due to the complexity of these effects, adaptive tuning of the uncertainty based on ML is getting popular. Our team demonstrated this potential by an experiment of a loosely coupled GNSS/INS integration. This experiment took place in an urban canyon with commercial GNSS and MEMS INS. Different ML algorithms are used to classify the GNSS positioning errors into four classes: healthy, slightly shifted, inaccurate, and dangerous. These are represented as 1 to 4 in the bottom of Figure 4. The top and bottom of the figure show the error of the commercial GNSS solution and the predicted classes by different ML. It clearly shows that ML can do a very good job predicting the class of the GNSS solution, enabling the integrated to allocate proper weighting to GNSS. Table 2 shows the improvement made by the ML-aided integration system.

This is just an example to preliminarily show the potential of ML in estimating/predicting sensors uncertainty. The methodology can also be applied to different sensor integration such as Wi-Fi/BLE/IMU integration. The challenge of the trained classifier may be too specific for a certain area due to the over-fitting of the data. This remains an open research question in the field.

Q: Machine Learning or Deep Learning for Navigation Systems?

Based on research in object recognition in computer science, deep learning (DL) is the currently the mainstream method because it generally outperforms ML when two conditions are fulfilled, data and computation. The trained model of DL is completely data-driven, while ML trains models to fit assumed (known) mathematical models. A rule of thumb to select ML or DL is the availability of the data in hand. If extensive and conclusive data are available, DL achieves excellent performance due to its superiority in data fitting. In the other words, DL can automatically discover features that affect the classes. However, a model trained by ML is much more comprehensible compared to that trained by DL. The DL model becomes like a black box. In addition, the nodes and layers of convolution in DL are used to extract features. The selection of the number of layers and the number of nodes is still very hard to determine, so that in trial-and-error approaches are widely adopted. These are the major challenges in DL.

If a DL-trained neutral network can be perfectly designed for the integrated navigation system, then it should consider both long-term and short-term challenges. Figure 5 shows this idea. Several hidden layers will be designed to predict the environments (or contexts) and the others are to predict the sensor uncertainty. The idea is straightforward, whereas the challenges remain:

Are we confident that the data we used to train the classifier are representative enough for the general applications cases?

What are the classes?

What are the features?

How many layers and the number of nodes should be used?

Q: How does machine learning affect the field of navigation?

ML will accelerate the development of seamless positioning. With the presence of ML in the navigation field, a perfect INS is no longer the only solution. These AI technologies facilitate the selection of the appropriate sensors or raw measurements (with appropriate trust) against complex navigation challenges. The transient selection of the sensors (well-known as plug-and-play) will affect the integration algorithm. Integration R&D engineers in navigation have been working on the Kalman filter and its variants. However, the flexibility of the Kalman filter makes it hard to accommodate the plug-and-play of sensors. The graph optimization that is widely used in the robotics field could be a very strong candidate to integrate sensors for navigation purposes.

Other than GNSS and the integrated navigation system mentioned above, the recently developed visual positioning system (VPS) by Google could replace the visual corner point detection by the semantic information that detected by ML. Looking at how we navigated before GNSS, we compare visual landmarks with our memory (database) to infer where we are and where we are heading. ML can segment and classify images taken by a camera into different classes, including building, foliage, road, curb, etc., and compare the distribution of the semantic information with that in the database in the cloud server. If they match, the associated position and orientation tag in the database can be regarded as the user location.

AI technologies are coming. They will influence navigation research and development. In my opinion, the best we can do is to mobilize AI to tackle the challenges to which we currently lack solutions. It is highly probable that technology advances and learning focus will depend greatly on MLs development and achievement in the field of navigation.

References

(1) Groves PD, Challenges of Integrated Navigation, ION GNSS+ 2018, Miami, Florida, pp. 3237-3264.

(2) Gao H, Groves PD. (2020) Improving environment detection by behavior association for context-adaptive navigation. NAVIGATION, 67:4360. https://doi.org/10.1002/navi.349

(3) Sun R., Hsu L.T., Xue D., Zhang G., Washington Y.O., (2019) GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System, Journal of Navigation, 72(3): 685-701.

(4) Hsu L.T. GNSS Multipath Detection Using a Machine Learning Approach, IEEE ITSC 2017, Yokohama, Japan.

(5) Yozevitch R., and Moshe BB. (2015) A robust shadow matching algorithm for GNSS positioning. NAVIGATION, 62.2: 95-109.

(6) Chen P.Y., Chen H., Tsai M.H., Kuo H.K., Tsai Y.M., Chiou T.Y., Jau P.H. Performance of Machine Learning Models in Determining the GNSS Position Usage for a Loosely Coupled GNSS/IMU System, ION GNSS+ 2020, virtually, September 21-25, 2020.

(7) Suzuki T., Nakano, Y., Amano, Y. NLOS Multipath Detection by Using Machine Learning in Urban Environments, ION GNSS+ 2017, Portland, Oregon, pp. 3958-3967.

(8) Xu B., Jia Q., Luo Y., Hsu L.T. (2019) Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX-and NMEA-Level Measurements, Remote Sensing 11(16):1851.

(9) Chiu H.P., Zhou X., Carlone L., Dellaert F., Samarasekera S., and Kumar R., Constrained Optimal Selection for Multi-Sensor Robot Navigation Using Plug-and-Play Factor Graphs, IEEE ICRA 2014, Hong Kong, China.

(10) Zhang G., Hsu L.T. (2018) Intelligent GNSS/INS Integrated Navigation System for a Commercial UAV Flight Control System, Aerospace Science and Technology, 80:368-380.

(11) Kumar R., Samarasekera S., Chiu H.P., Trinh N., Dellaert F., Williams S., Kaess M., Leonard J., Plug-and-Play Navigation Algorithms Using Factor Graphs, Joint Navigation Conference (JNC), 2012.

Excerpt from:
What are the roles of artificial intelligence and machine learning in GNSS positioning? - Inside GNSS

BOSTON (PRWEB) December 09, 2020

PathAI, a global provider of AI-powered technology applied to pathology research, today announced the result of a proof-of-concept investigation into ML model prediction of HRD directly from H&E-stained biopsy slides. DNA damage repair pathways, such as homologous recombination, have essential roles in healthy cells, and mutations in these pathways are closely associated with an increased risk for cancer, as well as cancer progression. HRD results from mutations in BRCA1/2, as well as other genes that encode the homologous recombination components that are responsible for error-free repair of double-strand breaks in DNA. HRD tumors are sensitive to poly-ADP ribose polymerase (PARP) inhibitors and platinum-based chemotherapy, making determination of a patients tumor HRD status clinically important. Genomic sequencing is currently the gold standard to classify a tumor as HRD or homologous recombination proficient, but this method has a high error rate, leaving a great unmet need to develop robust and reliable HRD scoring tools.

Identifying the underlying molecular drivers of cancer has tremendous significance not only for our fundamental understanding of the disease biology, but because these image-based assays may also play an important role in making patient treatment decisions in the future, like choosing the most effective therapeutic, said PathAI co-founder and Chief Executive Officer Andy Beck MD, PhD. Our ability to find these signatures in widely available H&E images suggests that our models could have a great impact, and we look forward to investigating this further and validating these results in future studies.

PathAI used two different approaches to predict the HRD status of a tumor from the H&E-stained tissue biopsy. Models were trained using breast cancer tumor biopsy images from TCGA and HRD scores of these same biopsies generated by Knijnenburg and colleagues (published in Cell Reports. 2018. 23:239-254). The Human Interpretable Features (HIF)-based model was trained using thousands of expert pathologist annotations of cell- and tissue-level features of the TCGA images to predict HRD status from HIF-based correlations, whereas the end-to-end model learned to predict HRD status directly from the biopsy image.

Both models predicted HRD with high accuracy, with the HIF-based model having an AUROC of 0.87, and the end-to-end model a AUROC of 0.80. The HIF-based model also revealed that HRD tumors have greater degree of necrosis and also more lymphocytes within the tumor itself than homologous recombination proficient tumors. These results show the enormous potential for digital pathology to identify clinically-significant genomic phenotypes that could not be detected using traditional pathology methods. PathAI will continue to develop and validate these important models for future clinical application.

About PathAIPathAI is a leading provider of AI-powered research tools and services for pathology. PathAIs platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine and deep learning. Based in Boston, PathAI works with leading life sciences companies and researchers to advance precision medicine. To learn more, visit https://www.pathai.com.

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PathAI Present Machine Learning Models that Predict the Homologous Recombination Deficiency Status of Breast Cancer Biopsies at the 2020 SABCS - PR...

Patently Apple has been covering the latest Project Titan patents for years, including a granted patent report posted this morning covering another side of LiDAR that was never covered before. While some in the industry have doubted Apple will ever do anything with this project, Apple has now reportedly moved its self-driving car unit under the leadership of top artificial intelligence executive John Giannandrea, who will oversee the companys continued work on an autonomous system that could eventually be used in its own car.

Bloomberg's Mark Gurman is reporting today that Project Titan is run day-to-day by Doug Field. His team of hundreds of engineers have moved to Giannandreas artificial intelligence and machine-learning group, according to people familiar with the change.

Previously, Field reported to Bob Mansfield, Apples former senior vice president of hardware engineering. Mansfield has now fully retired from Apple, leading to Giannandrea taking over. Mansfield oversaw a shift from the development of a car to just the underlying autonomous system.

In 2017, Patently Apple posted a report titled "Apple's CEO Confirms Project Titan is the 'Mother of all AI Projects' Focused on Self-Driving Vehicles." For more read the full Bloomberg report.

Like with all major Apple projects, be it for a head-mounted display device, smartglasses, folding devices, Apple keeps its secrets and prototypes under wraps until they've holistically worked out their roadmap.

That's why following Apple's patents is the best way to keep on top of the technology that Apple's engineers are actually working on in some capacity within the various ongoing projects. Review our Project Titan patent archive to see what Apple has been working on.

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Apple's SVP of Machine Learning & AI John Giannandrea has been assigned to Oversee Apple's Secretive 'Project Titan' - Patently Apple